Holistically-Nested Edge Detection with OpenCV and Deep Learning

Holistically-Nested Edge Detection (HED) is a deep learning-based method for detecting edges in images using convolutional neural networks. First introduced by Xie and Tu in 2015, HED has gained popularity for producing accurate and high-quality edge maps by learning edge features directly from image data.

In this article, we will explore the basics of HED, how it works, and how to implement it using OpenCV and deep learning.

What is HED (Holistically-Nested Edge Detection)?

Edge detection is an important computer vision task that involves identifying sharp discontinuities in images. These edges serve as the foundation for more complex processes like object segmentation and detection.

Traditional edge detection methods rely on hand-crafted features and heuristics, which often struggle with complex structures or variations in texture and lighting. HED overcomes these limitations by using a deep convolutional neural network (CNN) to learn edge features directly from data, creating hierarchical edge maps at different scales.

Understanding the HED Architecture

Implementation with OpenCV and Deep Learning

To implement HED, you need the pre-trained model files: deploy.prototxt (architecture) and hed_pretrained_bsds.caffemodel (weights). Here's a complete implementation ?

import cv2
import numpy as np

# Load the pre-trained HED model
# Note: You need to download these files from the original HED repository
hed_model = cv2.dnn.readNetFromCaffe("deploy.prototxt", "hed_pretrained_bsds.caffemodel")

# Load and preprocess the input image
image = cv2.imread("sample_image.jpg")

# Resize for faster processing
height, width = image.shape[:2]
max_size = max(height, width)
scale = 1.0
if max_size > 1000:
    scale = 1000.0 / max_size
resized = cv2.resize(image, None, fx=scale, fy=scale)

# Convert to grayscale for Canny comparison
gray = cv2.cvtColor(resized, cv2.COLOR_BGR2GRAY)
canny = cv2.Canny(gray, 100, 200)

# Create blob for HED model
# Mean values are ImageNet dataset means
blob = cv2.dnn.blobFromImage(resized, 
                            scalefactor=1.0, 
                            size=(width, height),
                            mean=(104.00698793, 116.66876762, 122.67891434), 
                            swapRB=False, 
                            crop=False)

# Perform HED edge detection
hed_model.setInput(blob)
hed_output = hed_model.forward()

# Post-process HED output
hed_output = np.squeeze(hed_output)
hed_output = (hed_output * 255).astype(np.uint8)

# Apply threshold to get binary edges
_, hed_edges = cv2.threshold(hed_output, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)

# Display results
cv2.imshow("Original Image", resized)
cv2.imshow("Canny Edge Detection", canny)
cv2.imshow("HED Edge Detection", hed_edges)

cv2.waitKey(0)
cv2.destroyAllWindows()

Key Advantages of HED

Model Requirements

To use HED, you need to download the pre-trained model files ?

• deploy.prototxt Contains the network architecture definition

• hed_pretrained_bsds.caffemodel Contains the pre-trained weights

• These files are available from the original HED GitHub repository

• The model was trained on the BSDS500 dataset for edge detection

Common Use Cases

HED is particularly useful for ?

• Object segmentation Precise edge maps help in separating objects

• Medical imaging Detecting boundaries in medical scans

• Autonomous vehicles Road and object boundary detection

• Image processing pipelines As a preprocessing step for complex tasks

Conclusion

Holistically-Nested Edge Detection offers superior edge detection capabilities compared to traditional methods by leveraging deep learning. It produces high-quality, multi-scale edge maps that are robust to variations in lighting and texture, making it valuable for various computer vision applications.